Litigation Risk Modeling for Finance Professionals with David Perla

Do you believe financial models are the number one decision making tool for corporations?

My short answer is no. Financial models are an indispensable tool in decision making for corporations. I don't think it's the number one tool. My suspicion is the number one tool is probably still the human brain.

Modeling is really important and in particular managing that risk.

What's fascinating about it is David Perla is vice chair at Burford Capital and a recognized innovator in the legal industry. He's the co founder of Pangea3, a pioneer in legal outsourcing and former president of Bloomberg Law. Named one of the American lawyers top 50 innovators of the last 50 years, David brings a wealth of insight on legal, tech, entrepreneurship and the future of law. When people are arguing over, for instance 3 to 5 million dollars, it may.

Not be rational, but it's predictable.

There aren't a million or 10 million of the types of cases we invest in.

How often do you accept in a client? Are you rejecting a certain percentage?

The short answer is we end up funding significant minority of the opportunities that come to us.

The teams are building these models. Are they building them in Excel? Do you have a special software you use or how's that work?

That's a widely known secret among modelers.

How much time do you actually spend in Excel? I'm just kind of curious every day. Will AI build the models for us in the future? Do you think you get to that point? Financial Modeler's Corner is the world's premier modeling podcast. It is brought to you by Financial Modeling Institute, the world's leading financial modeling accreditation organization. Welcome to Financial Modeler's Corner. I am your host, Paul Barnhurst, AKA the FP and a guy. This is a brand new podcast where we talk all about the art and science of financial modeling with distinguished guests from around the globe. The Financial Modelers Corner podcast is brought to you by by the Financial Modeling Institute. FMI offers the most respected accreditations in financial modeling and that's why I completed the Advanced Financial Modeler this week. I'm thrilled to welcome on the show David Perla. David, welcome to the show.

Paul. Thank you. I'm thrilled to be here and I appreciate you having me.

I have to ask, as a lawyer, did you ever think you'd be on a modeling podcast?

I absolutely did not. And on the ride over here I was thinking, I hope I can keep up with your audience in terms of our modeling and our financial acumen.

Well, I think it'll be great for audiences something different. We like to cover a lot of different things. Let me share a little bit about Dave's background and then we'll get into the questions. So David is Vice Chair and a member of the management committee. In his role at Burford, Mr. Perla is responsible for marketing, public policy, industry affairs and public relations. An entrepreneur and legal industry leader with expertise in building high growth legal and technology driven businesses, he was named a Top 50 innovator of the last 50 years by the American Lawyer. Prior to joining his current company, he was president of Bloomberg BNA Legal Division, Bloomberg Law where it oversaw legal and related products including its flagship Bloomberg Law Enterprise legal news informations and tools platform. He also co founded and was the co CEO and a director of Pangea3, the top ranked global legal process outsourcing provider. Pangea3 was acquired by Thomson Reuters in 2010 and grew to over 1,000 employees globally under his leadership. Before launching Pangea3, he was vice President of Business and legal affairs for Monster.com. he began his career in the New York office of Catton Munchen. Welcome again to the show.

Thank you.

Alrighty. Well, let's start with this question. You guys are a legal finance company. Can you tell our audience what that means, what exactly you do? And just give us a little background of of your guys company?

Certainly legal finance is a solution for both law firms and the clients they serve. And in addition Paul, it's a solution for some of the third parties that operate in what we think of as the legal industry as opposed to just the legal profession. So the easiest way to think about it for modelers or for people in the finance world is we help companies, we help law firms, and we help intermediaries to understand legal risk, to value legal risk and to monetize legal risk or use legal risk to offset expenses. So we can do that either via paying fees and expenses to pursue meritorious claims on behalf of companies or law firms, or, or we can use it to create liquidity by monetizing claims or by monetizing successful claims that have become what we call judgments but that take oftentimes many years to collect. So we are in some ways an investment bank for law.

That's an interesting way to look at it. It makes a lot of sense. So an investment kind of bank for law. And what's the typical time to someone may need help? Like your company comes to you, they have a claim that's legit, you know, from when they start through that whole legal process to when a judgment happens and they get paid out. How long is that? Typically I'm sure it varies a lot, but what kind of range are we looking at?

So it does vary, but most clients come to us early in the process. They're either considering a claim, they, they have, they've been harmed in some way, usually to the tune of many tens of millions of dollars. Our business is commercial legal finance, so we only work with large commercial types of entities. And the law firms that represent them, they may come to us very early. They have a damage and they are working with a law firm, but they haven't yet filed the suit all the way to. They've already litigated, but they have a judgment and everything in between. And that in between can be years. Usually it's at least a few years from the time you file until the time that you actually have some sort of a resolution. They may come to us to fund the expenses of litigating that. And those expenses include law firms. They can include economic experts, other types of experts, or they will come to us because they want to create liquidity. They, they want an advance against the potential outcome of that litigation once we're involved. PAUL what we have found over the course of our 15 plus year history is from the time we get involved, on average, it's about two and a half years from the time we deploy funds or deploy capital against a claim or a portfolio of claims until the time we see a recovery for Burford, and that's in our public filings for a public company. But some of them, because that's an average, some of them are much faster where we are advancing funds against a judgment and we have to help collect that judgment, some are much slower. There's, there's examples where we've been had our capital deployed for five, six, seven years, and we're still working with the client in helping them get an outcome. The whole idea, though, is for the client, we're bearing that duration risk. And that's part of the modeling we do is modeling for that duration risk. We bear that risk and we take that risk off of the client's hands.

Yeah, no, that's huge, that duration risk, if it's six, seven years before they collect. So client comes to you, you think they have emeritus claim. Sometimes you're paying them out a fraction of what the judgment may be. Sometimes they just want the legal fees. How at the end, do you collect your money? You collect when they get paid, and do you just put a kind of a fee on top of it or kind of, how does that work for you guys to recover your money and profit? Like what's kind of the model there?

Everything we do is on a non recourse basis. So your question's the absolute perfect question, which is, if we're non recourse and we're not debt or not equity, how exactly do we make our money? The short answer is up front. When we determine that we are going to finance a case or a group of cases, what we in our business call a portfolio, we will come up with a structure. We'll agree with our client as to how we get paid back and on what terms. So there are really three ways that we can get paid back, and oftentimes it's a combination or a hybrid. So normally the way the business works is most companies like us will get what's called their investment back. You'll hear the term ib. So we'll get our capital back plus either or both of some multiple of that investment back or some amount of money determined by the recovery that the client obtains. Okay, so we'll call that a percentage or a hybrid. And in both of those cases, either the multiple or the percentage of the outcome that the number can rise over time or it can rise with milestones. So it may well be that we would get our investment back plus a multiple up until the point where the case goes to trial, for instance. But if it goes to trial and it doesn't settle, that multiple might go up. Okay. Or if it goes to trial and that doesn't settle, we would get then either a higher multiple or a percentage of the ultimate award. And that percentage also might go up either over time or because of milestones. And all of that is modeled upfront, and all that is agreed to upfront. So, you know, I laughed at the beginning of our conversation about did I ever think I'd be on a podcast about modeling? And everything we do depends on modeling because we have to think about what are the types of milestones and what are the types of outcomes and how do we get paid back. But also, Paul, how do we make sure that our client is incentivized properly to behave in a rational economic way? We want to make sure if the case can be settled at an appropriate number, that it will be settled. But also if a case can't be settled at the right number and the better outcome would come from a trial, that the client is incentivized not to settle too early or for too low a number. So we're thinking about all of that at the very beginning of the potential matter.

Yeah, a lot of unknowns, lot of uncertainty in that, so I can totally understand. Modeling is really important, and in particular, managing that risk. You have no guarantee. If there's no settlement, they lose the. The trial. You may not be getting anything at the end. So how do you, you know, let's start with how you manage risk. I mean, obviously you're doing modeling. Are you doing Monte Carlo, a lot of sensitivities, or how do you think about that outside of the contract? I'm sure there's legal things you do to manage that risk. Like you mentioned, the milestones and the way you set up payments. But just in general, how do you think about modeling the risk we think.

About is a set of possibilities or set of nodes. And we have dozens and dozens of nodes in our financial model. And that helps us calculate how we want to obtain a return and negotiate that with the client. But those nodes are also important for how we value the case for carrying purposes. Because we're public, we have to carry these on our books as a, as an investment, for purposes of the sec. And so we have to think about both of those purposes to the model. So our model, which is dozens of nodes, looks at all the elements of risk that go into, on the one hand, pricing, on the other hand, valuation. We've learned that over the course of our history. And we then have to weight each of those. We have to sort of put not just a value, but what is the percentage likelihood that the case will settle at a particular point in time? But what happens to that percentage if certain other things happen in the case earlier? So later nodes get impacted by earlier nodes, which is not a surprise to anyone who's taken statistics or modeling. You know, we then add on. We do do Monte Carlo modeling. We want to layer in a more objective form of modeling, and then we want to compare. We want to compare against other cases, and that goes into the model. So we may come out with a model one particular way and say, well, how does that model compare to what we've done on similar cases and what facts are different? What's changed? Maybe the court is different. The particular judge may give us a different outcome within the model, or it might give us different weighting or a different percentage. It could be that the lawyers, you know, it could be the defendant who's on the other side. Is it. Is it a company that settles early and settles it at a number that is appropriate for an early settlement, or is it a company that will only settle early for a low number and will always try the case that's going to impact not merely the duration and that node, but it's going to impact where we weight the earlier nodes and what's going to happen. So all of that is done by our modeling team, which are traditional quantitative analysts, people with math degrees, statistics degrees, in some cases physics degrees, but people you would think of as quants.

Sure. Yeah. I was going to say you have to have some pretty strong quants with the Monte Carlo, the number of variables, everything you're doing. It reminds me sometimes a little bit like underwriting and some of those type of things, some similar.

It is underwriting, Paul. And what's fascinating about it is our quantitative analysts have learned enough law that they can sit with our underwriters, who historically are lawyers, and the lawyers have learned enough about modeling and the mathematics and the statistics to be able to create something that's greater than the sum of those two parts in terms of how the model works, in terms of how we think about the case. The other thing that it's allowed us to do is it's allowed us to take data from public sources and our own proprietary data and use both in terms of how we model.

Yeah, got it. Interesting. So, Jose, a client comes to you like, how often do you accept the clock? You got to do the modeling, make sure it makes sense. How often do you accept in a client, or are you rejecting a certain percentage, saying, hey, this is just way too risky. We've run the numbers, and it doesn't make sense for us to fund this.

A few things happen. The short answer is we end up funding a significant minority of the opportunities that come to us, whether we seek them out or they seek us out. And by that I mean, if you add up things like inbound emails, the phone rings, all the meetings we go to where people say, I'd like you to look at something, and it goes in into what we think of as intake. We're funding less than 10% of that.

Okay, so that's a pretty small number.

It's a very low number. And that is industry standard within the complex commercial litigation finance industry to be funding less than 10%. However, once something gets into the formal underwriting process, we sense that it meets certain criteria. We like the merits of the case. We like the facts, you know, facially. We think the counterparty is credible. We think they're in the right, for lack of a better way of putting it, we think the defendant is credit worthy. We don't want to be in the credit risk business. We want to be in the litigation risk business. So we think the defendant is credit Worthy. We think it's in the right forum, in the right jurisdiction. Things that lawyers think about, then it's a higher percentage of cases that we will actually fund. But we're going to undertake a rigorous underwriting process which can take anywhere from a few weeks to a few months. That includes all the legal research, all the financial modeling, all the negotiation, all the structuring of the terms. It looks like any other financial transaction. Underwriting and negotiation.

Yeah. You mentioned investment bankers. Obviously a lot of M and A there, but a similar type thing in the sense of. I'm sure there's some back and forth discussion. You're talking large dollars. I imagine, you know, these cases are millions, tens of millions, maybe even hundreds of millions sometimes.

Yeah. Generally speaking, we are only involved if the amount in dispute is significantly in excess of US$50 million.

Makes sense.

And so the way to think about the businesses legal fees and expenses, the cost of litigating is often about 10% of the value of the case or the likely damages of the case. Any number higher than that. And it's very hard to use legal finance. So if $100 million likely damage is going to cost $20 million to litigate to get to a successful outcome, that's not a bad case, Paul. It's just not one that likely supports financing. When we account for the fact that we need to make a return and the counterparty needs to behave in a way that's economically rational, we will use in the businesses term headroom. There has to be enough headroom for Burford to make its money, for the law firm to get paid and for the client to recover enough money that the client is happy and that that headroom number tends to be about 10 to 1 from an investment perspective.

Got it. That makes sense. So just to make sure I got it right, you know, you have the hundred million dollar. Let's just say that's what you think you're going to settle at. That's what you estimated legal costs are going to be in that 8 to 12 million range. So it's roughly 10 times. That's something you would consider. You want that headroom of at least 10 times. So you want 10 million or less.

Ideally, that's correct. And the reason we operate at a higher number in the market is twofold. Number one, we're very large on a given year. We deploy, give or take about a billion US dollars and our portfolio is about $7.4 billion. The total value of what we've invested plus the kind of the markups of it the value of the portfolio. The other reason is in litigation, we have learned that the settlement dynamics and the resolution dynamics from a modeling perspective look different as the case numbers get very small. So when people are arguing over $100 million, to follow your example, that tends to be an economic decision. There are business decisions in terms of how they run their business, but those are economic actors. When people are arguing over, for instance, three to five million dollars, those are often smaller businesses. Those are often people less familiar with litigating the dynamics of settlement and how those litigate tend to be very, very different, even if that ratio holds up. So it's not merely that we'd have to do a lot more of them, it's that they behave differently than the larger cases.

While my background is in FP and A, I am also passionate about financial modeling. Like many financial modelers, I was self taught. Then I discovered the Financial Modeling Institute, the organization that offers the Advanced Financial Modeler program. I am a proud holder of the afm. Preparing for the AFM exam made me a better modeler. If you want to improve your modeling skills, I Recommend the Afghan AFM program. Podcast listeners save 15% on the AFM program. Just use code Podcast. There's more uncertainty and hence more risk because you're often doing dealing with people that have less experience and so they may not behave as rational. As someone who's been through it 15 times, you have a better idea of how they're going to behave and can model more of that risk and uncertainty.

That's correct. And. And they also may. A larger claim is often brought by a party that is treating it as an economic matter without emotion tied up into it or without other inputs tied up into it. We can model for really any economically driven decision making.

Sure. Yeah. When people behave rational, it's much easier to model it.

That's correct.

You know, behavioral economics. Right.

Even if we think of, you know, in traditional behavioral economics, really assumes there's no rational actor. If we, if we go back to sort of the old Torsky and Kahneman behavioral economics, you can still model for predictable irrational economic behavior. What you can't model for is idiosyncratic behavior. And so we try and avoid anything where we think people are going to behave idiosyncratically.

Totally agree. Right. There are certain things where it may not be rational, but it's predictable, like overdraft fees. From a smart financial standpoint, there's no rational reason you should be constantly using overdraft fees, but you can predict a certain amount of the population for various reasons, will use them.

Correct. The piece we can't do for, for individual cases is we can't undertake any actuarial modeling. We can model industries on a thematic basis. So I'll use the term actuarial, Paul. We can model general trends on an actuarial basis, but in traditional complex commercial litigation, it's nearly impossible to undertake actuarial modeling on an individual case, which makes case predicting very, very difficult to do. With something like artificial intelligence, everyone's talking about AI, and AI has tremendous value in what we do, but when people say, well, it can predict the case, our cases are sufficiently large and unique that to try and tailor a large language model to predicting the outcome would be to say, well, we could create an actuarial model and you just can't with large scale complex commercial litigation.

Well, I would imagine each of them are so unique. There's not a big data set either out there. Right. Large language models want to be trained on a huge amount of data, and there's very limited data. And each case has its own idiosyncrasies to it.

That's correct. So, number one, there aren't a million or 10 million of the types of cases we invest in. But even if there were, the facts differ from case to case. Right. There are no slam dunks in terms of it being, you know, 100% the claimant is going to win or 100% the defendant is going to win. Usually when they're that clear at the beginning, they settle long before anyone comes to talk to a Burford about financing, if they're that obvious. There's occasionally a claim where a client comes to us and it's simply a matter of a very well heeled, very well capitalized defendant and a claimant that doesn't have money, but it has a slam dunk case. Usually it's not quite so clear and the underwriting involves facts and the application of the law to those facts and the law may vary in jurisdiction to jurisdiction. So even if we had a large volume of cases, you'd have these idiosyncratic facts. If I were to contrast that. Let me give you an area where I think AI is going to play an enormous role. Yes, it is personal injury. So if you look. And the reason. How do we know that? If you look at insurance in the property and casualty area and in. And in the personal injury area, insurance for d decades has been using actuarial models to figure out what the claim value is and what to pay and what to offer. And I did insurance Defense work for a summer when I was in law school, we did airplane crashes. We knew what injuries were worth depending on where the person lived, their age. What they did for a living was known 30, 35 years ago, already long before we had artificial intelligence tools. But it's easy to sort of see. You have a large volume of cases, many of them, the outcomes are known, not all. Many settle. But it's not terribly hard to get the settlement data. And even anecdotally it's relatively straightforward to get that data versus in the commercial world. If we look at settlements and something like 80 to 90% of complex commercial claims filed end up settling, so they end up resolving without the resolution being public. The only settlement data we can look to is our own data. There is no public database of settled commercial cases precisely because they're confidential.

Yeah, they don't want that information out there. They don't want you to know the amount they settled for and all the details and what the client can say and can't say and because often it doesn't look good, sometimes it might, but you know, it's just private. They don't want the numbers to be known.

It's just private. If you flip that, if you look at insurance, the reason insurance was able to do it for so many years at the industry side was every insurance company globally has always known what it settles for and they have a massive volume. But even the personal injury firms, some of them are very, very large, some of them are doing thousands of cases a year. They have a fairly robust set of data around not only what a case is settled for, what's the value of an injury or a harm in a particular place, meaning particular criteria, but they also know what that value is by insurance company. So if you talk to personal injury lawyers, they could tell you here's what you know, this insurance, you know, here's what a GEICO will settle at versus here's what a progressive will settle at. They know that information because it's such a high volume business and there's, there are things in between but that, but the more idiosyncratic the claim becomes and the larger it becomes, the less likely that we'll be able to model it on a actuarial basis. And therefore the less likely we are going to be able to run AI driven models. It's not to say we can't use AI, we use plenty of AI, just not on outcome based model, not on predicting the outcome.

Sure. You know, and something you said I thought was interesting when you talk, you know, Personal injury. And you know, the geico's the insurance of the world. Right. I can get information going out there to know who's good at paying claims and who usually offers an amount. Right. There's enough information out there. You see even Consumer Reports and different people saying, from a customer service supporting your claim, these ones are best. You generally get the highest percentage and you can make your choices accordingly. Where. Yeah, you don't have that in your industry. It's amazing how much I can even just figure out going out on the Internet about that kind of stuff today.

That's exactly right. And property and casualty insurance is the best example which you use. Most people know they're insuring. In the case of my house, I live in a very old house. I'm insured with a company that will pay, but it's also a company where you don't file a claim for the average damage to the house. You're using the insurance company I use in case a major problem happens because I have a very old house that's very hard to rebuild. There are other, you know, car insurance. Most people view it as sort of a cost of owning a car. And so they're looking for the least expensive. They're not worried about the case, well, what if my car gets totaled? Right. Because they have to insure their car. And so they're saying, how do I get the cheapest price? Even though I think I'm really not going to use the insurance. It's very different than owning a house.

Yeah, no, all great examples. Right. There's a lot of difference there. And sometimes when it's required, okay, what's the cheapest I can get? Other times like, okay, this is the biggest investment in the world. It's my house. I want more. You know, you're going to make different behaviors. I'm curious. Talking to AI, I know you wrote an article about kind of, you know, the current and future state of AI and legal finance. You mentioned, you know, there's definitely areas you can't use it. What areas have you most excited about AI? What are you most excited about in your field?

I think we're very excited about the ability to find cases that are amenable to legal finance solutions and where the claimant would likely want to finance their claim if they knew about what we did. So we're still a nascent industry and so we think there are still cases out there where claimants or their law firms don't necessarily know that they've got a claim that they can either monetize or where we could pay the fees and expenses. So from an origination standpoint, we think AI is very powerful, where, you know, much more powerful than simply sending emails, right, to lawyers or to companies. So we're using it to identify potential cases once filed. We are also seeing a lot of exciting use cases where technology companies are now finding behaviors and patterns in the public sphere that give rise to meritorious claims of damage. So in other words, they're finding harm that wouldn't otherwise have been surfaced, and they're able to say to the groups of people or the corporations, you've been harmed. And number one, you can have that harm addressed right. And you can be made whole for that. But number two, you can also turn that harm into a legal asset via the use of legal finance. So those are the two areas on what I'll call the front end of our business. Paul. The other areas, just when I talk about our modeling, our own models are now sitting on technology platforms where our ability to model them faster is dramatically improved with the use of artificial intelligence tools or even just software tailored towards modeling. And AI is helping us sort of write through that code and write through those models. So that's a third area. The other reason I wrote about it was there's a lot of attention in the litigation space to this perceived holy grail of predicting the outcome, and I just don't think it's real. And I think the more people write about something that's not real, it does a disservice to the actual use of artificial intelligence in ways that are helpful and thoughtful for both sides. Whether you fund affirmative litigation as we do, or whether you're on the defense side or you're an insurance company. We want people to know there's a real value and use to AI. But when people are writing things that I think are just silly about, well, we can predict that this case is going to win, sure, if it's a personal injury case, to my earlier point, but when you start saying things like that in complex commercial litigation, it calls into question the credibility. And then in our view, we needed to address that, to say there really are uses. But don't get distracted by this outcome that if it ever were to happen, it's decades away. This predictive modeling.

Yeah, got it. No, that's helpful. Well, I want to ask a question. You mentioned software and different things. What are your. The teams that are building these models, are they building them in Excel? Do you have a special software you use, or how's that work?

So we use, you know, Everyone uses Excel. It's the thing everyone loves to hate and loves to use. We also use Pythons, so most of our quants can code. That's sort of a widely known secret among modelers. I suspect most of the people listening to this are going to say, yeah, I can write code also. So they are just for the, for the straightforward models in Python. And the reason we're doing that is it allows us to do anything we want with the data and to manipulate the data. And then we use a series of financial software tools in our finance and quant department that are, you know, third party tools.

Yeah, got it. I'm sure. For the risk modeling, whatever else, Monte Carlo, all those type of things.

Correct, correct. But you know, look, we are like most organizations in the finance world, incredibly conversant in Excel and I expect us to remain incredibly conversant. You know, my, my son's going to business school next year and I joke with him, you know, my world is horizontal, not vertical. Yeah. If I'm not in Excel, I'm often in PowerPoint and you know, otherwise I'm writing emails. But, but we're not really writing narrative documents.

Sure. And I'm curious, how much time do you spend kind of reviewing models in Excel doing things? I know you're in the law position here, you're a lawyer, but you're also helping run the business. You're not doing the modeling. How much time do you actually spend in Excel? I'm just kind of curious. Well, you may know me as the FPA guy. What you may not know is that I am also passionate about financial modeling. Like so many other financial modelers, I was self taught. I didn't know that there was an organization dedicated to advancing the discipline of financial modeling until I discovered Financial Modeling Institute. FMI offers the Advanced Financial Modeler Accreditation, a program designed to strengthen and validate your financial modeling skills. I am a proud holder of the AFM accreditation. Preparing for the accreditation exam made me a quicker and more confident modeler. If you want to become a better modeler, I can't Recommend the AFM AFM program highly enough. Podcast listeners save 15% on the AFM program. Just use code podcast@fminstitute.com podcast so on.

The model side, it's probably hours per week. Every potential matter is modeled and so you have to be able to review the model. When we look at what we call commitment memo or commitment committee memo, it's got the model built into a to a narrative overview of the case and all the scenarios you have to be able to read the model. I prefer to read it in Excel, you know, the underlying raw data. But in terms of how we use Excel every day, there's not a day at a place like Burford where I'm not looking at an Excel spreadsheet of one type or another. They're not all models.

Sure.

But Excel is the. It's the lingua franca of how we sort of communicate the numbers at the business.

Yeah. And so leads me a question. When you're looking at a model, let's say you're evaluating a case, what lets you know that, hey, they've done a good job modeling what type of things you like to see to make it easy to read the model. Are there certain things that make it, you know, easy for you as someone who's consumer in that sense? Right. You haven't built the model, but you got to use it to evaluate things. What do you kind of look for?

So we've evolved and we've been modeling for many, many years already. I think, number one, our models are dynamic and so the presentation has to look somewhat different than the underlying data. So I love being in the spreadsheet. But when it presents, it has to present in a way that allows someone to change a variable and see how that ripples through, which means it also has to render all the appropriate variables without looking at, let's say there were 70 nodes. The average person can't look at 70. So one of the important things is which are the models we need to present? So the settlement node is always going to be really important if we're early on in the case. What's the summary judgment node? What's the node that says we're going to go to trial? What's the node that says we can win a trial? What's the appellate node? Those things became very important over time. Beyond that, we have some variability in the models depending on the type of case, in the subject matter of our case, what we think of as our pipelines are going to look different. And we also have certain expectations as to within a type of case, how the model needs to treat certain things differently. So let me give you an example. In the international arbitration world, it's a very unique world. It is very long durations. So if a client or its law firm comes to us and says, we think it's worth this amount of money, but we're confident it's going to settle in two years, unlikely that model is going to render an outcome we like because it's a much longer Duration type of an industry, the international arbitration, when you're arbitrating against sovereigns or under treaties, just takes much longer. Another area where the models had to account for differences is in the patent world. The appellate rate, the percentage rate by which things are reversed on appeal is higher in patent than in other areas. And everyone knows that going in. And so you have to look carefully and say, well, is this more or less than we normally see in patent? If you saw the model for a patent case, if you saw the same numbers come out of the model for a breach of contract case, we would be much less optimistic about that breach of contract case. We would say, why is the risk on appeal so high? Whereas in patent it's just a built in risk in the patent world that things get reversed and then they have to get, you know, there's multiple lines of appeal. And so we're used to that. We would not like those numbers. We would not like that outcome in a different field of law.

Fascinating. I think I could probably go on for quite a while asking questions, but I know we're nearing the end of our time. Like I said, you're the first person we've had in legal finance on the show and I found it really fascinating just to learn a little bit about it because obviously this isn't something I've ever modeled or even ever really even thought about. So you reached out and mentioned coming on the show. So this has been great to just learn a little bit about it, a little bit of AI, how you think about things. And I have a couple kind of questions I just want to get your thoughts on. Fortunately, you did not get the long list of typical questions I ask in rapid fire for modeler, like circular references, yes or no vba. I figured I'd spare you all.

Thank you.

You're like, yes, thank you. But I will ask a couple. So I want to get your thought. You think Excel's ever going to die?

No, I had this whole long answer plan, but no, I think, I think Excel's here to stay. Think people actually like it. They know how to work with it. I think they know how to write code and macros around it and AI knows how to help them. So I, I think, I think we're with Excel for, you know, I'm 55, so at least my lifetime, I had.

One person respond with, yes, it just can't be in my lifetime, you know, like, don't make me learn a new tool.

I won't live long enough to see it.

So, and just in general, you Know, will AI build the models for us in the future? Do you think you get to that point in general?

I think so. I think we're there. By the way, I think it's building models today. What it's not doing is building the models in a way that lets us see what it did to make sure they're built the way a modeler would build them. So there's not that confidence because AI is by definition a black box. Right. An LLM is trying to predict what it thinks you want. It's great for a speech because you don't care how the speech got written. Even today, most people don't care if it's plagiarized. But for a model where you do need to know what's underneath it, there's not that confidence level yet. But I think it's building plenty of models today and it's going to get better and better. And I think you'll see some hyper specialties where for particular areas, once people have that confidence, they're going to layer on top of the model. I mean we used it, we built a web scraper for something and, and we hired a student to do it and she used AI to help write the code. But we could see the code. We weren't looking for an original work of authorship. So why wouldn't we let it write complex models as long as we have confidence in them?

Yeah, I think you make a great point. It's the confidence, the black box. It's getting to the point where we can see all the pieces that go into it. There's definitely areas where it can take something and predict out or do some modeling and things and it will get better with time. That's typically what we get. Most people say yes, last one and then we'll wrap up here. Do you believe financial models are the number one decision making tool for corporations?

So my short answer is no. I think financial models are an indispensable tool in decision making for corporations, without which the corporation is going to be at a massive competitive disadvantage and therefore ultimately be less successful than its competitors. But at the end of the day, I don't think it's the number one tool. My suspicion is the number one tool is probably still the human brain, at least for a few more decades. While businesses require strategic decision making and they require judgment in a way that the model can't yet get there. Do I think it's indispensable? Yes. I mean we just wrote a job description that, that three years ago, prior to GPT would have taken hours and hours of numerous people's time. It took me eight prompts and then a little bit of editing and it's largely done and it's really good. And I'm sure someone, not me, but I'm sure someone can use it to actually look through resumes and pick the ones that facially meet the criteria. Okay. Yep. Will it be broad enough? Probably not. But I don't want in any way diminish my hope and my optimism about models and about artificial intelligence merely to say I don't think it's the single tool that we can rely on.

Sure. No. And you're not the first to say that we get differing opinions. I love to hear what people think. So I appreciate you giving that view and some context on it. Now that was the last question I had. So just kind of wrap up. If someone wants to learn more about, you know, your company, maybe get in touch with you, what would be the best way for them to do that?

Number one, you can always write me@dpearlerfordcapital.com you can just go on Burford Capital, that's our website name of the company and there are also email addresses on there for who to write about specific things. But the website is a wealth of information on also to sort of help understand how it works and how we think through a claim or set of claims. Most people who call us have gone on our website to learn first, but by all means they can reach out directly as well.

Great. And next time we have you on, I'm going to give you all the modeling questions. Expect study up and we'll get you all geared up on modeling. You can come to Vegas with me for the Financial Modeling World Championship.

I'll come to Vegas with you and I'll bring one of my quants to give me the answers to the questions.

Yeah, it's your quants that should come to Vegas. I don't, I watch, I don't participate. But thank you so much for joining me. I enjoy having you on the on the podcast.

Thank you. This is a pleasure.

Financial Modeler's Corner was brought to you by the Financial Modeling Institute. This year I completed the Advanced Financial Modeler certification and it made me a better financial modeler. What are you waiting for? Visit FMI www.fminstitute.com podcast and use code podcast to save 15% when you enroll in one of the accreditations today.